Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search | Zendy

Pengxiao Zang | Zendy; Jie Wang | Zendy; Tristan T. Hormel | Zendy; Liang Liu | Zendy; David Huang | Zendy; Yali Jia | Zendy

Open Access

Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search

Author(s) -

Pengxiao Zang,

Jie Wang,

Tristan T. Hormel,

Liang Liu,

David Huang,

Yali Jia

Publication year - 2019

Publication title -

biomedical optics express

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.362

H-Index - 86

ISSN - 2156-7085

DOI - 10.1364/boe.10.004340

Subject(s) - segmentation , computer science , artificial intelligence , retinal , optical coherence tomography , convolutional neural network , ground truth , pattern recognition (psychology) , sørensen–dice coefficient , optic disc , graph , image segmentation , optic disk , computer vision , optics , ophthalmology , physics , medicine , theoretical computer science

Quantitative analysis of the peripapillary retinal layers and capillary plexuses from optical coherence tomography (OCT) and OCT angiography images depend on two segmentation tasks - delineating the boundary of the optic disc and delineating the boundaries between retinal layers. Here, we present a method combining a neural network and graph search to perform these two tasks. A comparison of this novel method's segmentation of the disc boundary showed good agreement with the ground truth, achieving an overall Dice similarity coefficient of 0.91 ± 0.04 in healthy and glaucomatous eyes. The absolute error of retinal layer boundaries segmentation in the same cases was 4.10 ± 1.25 µm.

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